The present invention relates generally to apparatus and techniques for inspecting a sample, such as a reticle, photomask, or other semiconductor materials or surfaces, and more specifically to apparatus and methods for determining whether a sample is defective.
A reticle or photomask is an optical element containing transparent and opaque, semi-transparent, and phase shifting regions which together define the pattern of coplanar features in an electronic device such as an integrated circuit. Reticles are used during photolithography to define specified regions of a semiconductor wafer for etching, ion implantation, or other fabrication process. For many modern integrated circuit designs, an optical reticle's features are between about 1 and about 5 times larger than the corresponding features on the wafer. For other exposure systems (e.g., x-ray, e-beam, and extreme ultraviolet) a similar range of reduction ratios also apply.
Optical reticles are typically made from a transparent medium such as a borosilicate glass or quartz plate on which is deposited on an opaque and/or semi-opaque layer of chromium or other suitable material. However, other mask technologies are employed for direct e-beam exposure (e.g., stencil masks), x-ray exposure (e.g., absorber masks), etc. The reticle pattern may be created by a laser or an e-beam direct write technique, for example, both of which are widely used in the art.
After fabrication of each reticle or group of reticles, each reticle is typically inspected by illuminating it with light emanating from a controlled illuminator. Optical images of one or more portions of the reticle are constructed based on the fraction of the light reflected, transmitted, or otherwise directed to a light sensor. Such inspection techniques and apparatus are well known in the art and are embodied in various commercial products such as many of those available from KLA-Tencor Corporation of San Jose, Calif.
During a conventional inspection process, the optical image of the reticle portion being inspected is typically compared to a corresponding reference image. Conventionally, the reference image is either generated from a circuit pattern data that was used to fabricate the reticle or from an optical image of a nearby area of the reticle itself. Either way, the optical image features are analyzed and compared with corresponding features of the reference image. Each feature difference is then typically compared against a threshold value. If the optical image feature varies from the test feature by more than the predetermined threshold, a defect is defined.
Mechanisms for a typical inspection process may include a number of serially coupled processors. The image data is fed into and processed by a first processor. After the first processor performs one step of the analysis, the resultant data is then fed into a second processor for the next step in the analysis. The image data may be fed serially into any number of processors. Typically, the different processors will each perform some small portion of the total analysis algorithm(s). The algorithms are usually hard-coded into the individual processors.
Although serially processing portions of the image data is adequate for some applications, it is too slow and/or inflexible under certain conditions. For example, as circuit patterns and corresponding reticle patterns grow more complex, the image data of such reticles grows to contain a relatively large amount of data that must be accurately analyzed. A typical reticle may be converted into 1 million by 1 million pixels of image data. Thus, it may become quite burdensome to process such large amounts of image data.
Additionally, conventional image processing is often dependent on the proper functioning of all of the processors. That is, if a single processor fails within the serial chain of processors, the image data may not be properly analyzed. The inability to properly analyze is especially likely if there are no other processors within the serial chain of processors that perform the failed processor's functions.
Finally, inspection systems that include processors with fixed or hard-coded algorithms often cannot handle the full range of possible algorithms that may be useful for image processing, and they are not easily upgraded or changed if a new set of algorithms is desired. For example, if new algorithms are desired, the processors may have to be replaced with new processors that have a new set of hard-coded algorithms. This procedure may be relatively time-consuming and/or costly.
Thus, improved inspection apparatus and techniques are needed. More specifically, mechanisms for more efficiently and accurately processing image data are desired. Additionally, flexible mechanisms for changing the processor algorithms are also desirable.